Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images

被引:8
|
作者
Kawata, Y [1 ]
Niki, N [1 ]
Ohmatsu, H [1 ]
Kusumoto, M [1 ]
Kakinuma, R [1 ]
Mori, K [1 ]
Nishiyama, H [1 ]
Eguchi, K [1 ]
Kaneko, A [1 ]
Moriyama, N [1 ]
机构
[1] Univ Tokushima, Tokushima 770, Japan
关键词
computer-aided diagnosis; evolution; pulmonary nodule; registration;
D O I
10.1117/12.431081
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a method to analyze volume evolutions of pulmonary nodules for discrimination between malignant and benign nodules. Our method consists of four steps; (1) The 3-D rigid registration of the two successive 3-D thoracic CT images, (2) the 3-D affine registration of the two successive region-of-interest (ROI) images, (3) non-rigid registration between local volumetric ROIs, and (4) analysis of the local displacement field between successive temporal images In preliminary study, the method was applied to die successive 3-D thoracic images of two pulmonary lesions including a metastasis malignant case and a inflammatory benign to quantify the, evolving process in the pulmonary nodules and surrounding structure. The time intervals between successive 3-D thoracic images for the benign and malignant cases were 120 and 30 days, respectively. From the display of the displacement fields and the contrasted image by the vector field operator based on the Jacobian, it was observed that the benign case reduced in the volume and the surrounding structure was involved into the nodule in the evolution process. It was also observed that the malignant case expanded in the volume. These experimental results indicate that our method is a promising tool to quantify how the lesions evolve their volume and surrounding structures.
引用
收藏
页码:1890 / 1901
页数:4
相关论文
共 50 条
  • [31] A Three-dimensional Detector Based on Focal Loss for Pulmonary Nodules Detection
    Wang, Lei
    Dai, Yaping
    Jia, Zhiyang
    Nie, Yongkang
    Liu, Liang
    PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, : 8445 - 8449
  • [32] Precise resection of multiple pulmonary nodules using a three-dimensional reconstruction model: A case report
    Chu, Xiang-Peng
    Chen, Zi-Hao
    Lin, Shao-Min
    Tang, Wen-Fang
    Zhang, Jia-Tao
    Lai, Yao-Ming
    Fu, Rui
    Qiu, Zhen-Bin
    Lin, Jun-Tao
    Nie, Qiang
    Yang, Xue-Ning
    Wu, Yi-Long
    Zhong, Wen-Zhao
    THORACIC CANCER, 2021, 12 (06) : 970 - 973
  • [33] Pulmonary Nodules: Growth Rate Assessment in Patients by Using Serial CT and Three-dimensional Volumetry
    Ko, Jane P.
    Berman, Erika J.
    Kaur, Manmeen
    Babb, James S.
    Bomsztyk, Elan
    Greenberg, Alissa K.
    Naidich, David P.
    Rusinek, Henry
    RADIOLOGY, 2012, 262 (02) : 662 - 671
  • [34] Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
    Shi, Junyi
    Xing, Fangliang
    Liu, Yang
    Liang, Tengxiao
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2023, (200):
  • [35] Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest
    Paing, May Phu
    Hamamoto, Kazuhiko
    Tungjitkusolmun, Supan
    Visitsattapongse, Sarinporn
    Pintavirooj, Chuchart
    APPLIED SCIENCES-BASEL, 2020, 10 (07):
  • [36] Image sequence compression using adapted three-dimensional transform: Application to scintigraphic images
    Ploix, A
    Vigouroux, B
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 1999, 15 (03) : 255 - 263
  • [37] Three-Dimensional Image Surface Reconstruction Based on Sequence Images
    Yang, Dan
    Qu, Zhong
    2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11, 2009, : 1902 - +
  • [38] Automated extraction of pleural effusion of three-dimensional thoracic CT images
    Kido, Shoji
    Tsunomori, Akinori
    MEDICAL IMAGING 2009: COMPUTER-AIDED DIAGNOSIS, 2009, 7260
  • [39] Characterization of three-dimensional structure using images
    Liu, Bin
    Goree, J.
    Ruhunusiri, W. D. Suranga
    REVIEW OF SCIENTIFIC INSTRUMENTS, 2015, 86 (03):
  • [40] Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images
    Kawata, Y
    Niki, N
    Ohmatsu, H
    Moriyama, N
    ACADEMIC RADIOLOGY, 2003, 10 (12) : 1402 - 1415